2021
DOI: 10.1080/08839514.2021.2004345
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Feature Selection Empowered by Self-Inertia Weight Adaptive Particle Swarm Optimization for Text Classification

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Cited by 11 publications
(11 citation statements)
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“…Ada-boost, SVM, and the NB classifiers were used to evaluate the feature subsets selected using AFSA and perform the classification. In [49], the standard PSO algorithm has been adapted and modified by adding a weighting mechanism called Self-Inertia Weight Adaptive Particle Swarm Optimization (SIW-APSO) to enhance the performance of TC. SIW-APSO has a fast convergence phenomenon that yielded high search competency and a better selection of features.…”
Section: B: Modified/improved Metaheuristic Methodsmentioning
confidence: 99%
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“…Ada-boost, SVM, and the NB classifiers were used to evaluate the feature subsets selected using AFSA and perform the classification. In [49], the standard PSO algorithm has been adapted and modified by adding a weighting mechanism called Self-Inertia Weight Adaptive Particle Swarm Optimization (SIW-APSO) to enhance the performance of TC. SIW-APSO has a fast convergence phenomenon that yielded high search competency and a better selection of features.…”
Section: B: Modified/improved Metaheuristic Methodsmentioning
confidence: 99%
“…The weighted uncertainty operator in PSO and the tuned parameter in KNN had achieved better accuracy than the standard technique. In [49], the adaption of the weighting scheme (Self-Inertia Weight) into the PSO has overcome the problem of premature convergence in standard PSO. Furthermore, the experimental results showed that the proposed (Self-Inertia Weight) has better text classification accuracy than the typical (IG, CHI) and standard metaheuristic methods (GA, PSO).…”
Section: Rq2: Does Applying Metaheuristic Algorithms For Tc Lead To B...mentioning
confidence: 99%
“…Recent focus of computer vision community is the use of deep-learning model [13][14][15] that are computationally expensive. However, at the same time, the research community is still widely presenting machine learning (ML)-based solutions [16][17][18].…”
Section: Literature Reviewmentioning
confidence: 99%
“…In addition to the classic particle swarm optimization [17][18][19] and genetic algorithm [20], some other novel bionic algorithms have been successfully applied to text feature selection. For example, the cat swarm optimization algorithm [21], artificial fish swarm algorithm [22], the Jaya optimization algorithm [23], the firefly algorithm [24],the grey wolf optimization algorithm [25]and the ant colony algorithm [26].…”
Section: Related Workmentioning
confidence: 99%